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This study focuses on the task of supervised prediction of aging-relatedgenes from -omics data. Unlike gene expression methods for this task thatcapture aging-specific information but ignore interactions between genes (i.e.,their protein products), or protein-protein interaction (PPI) network methodsfor this task that account for PPIs but the PPIs are context-unspecific, werecently integrated the two data types into an aging-specific PPI subnetwork,which yielded more accurate aging-related gene predictions. However, a dynamicaging-specific subnetwork did not improve prediction performance compared to astatic aging-specific subnetwork, despite the aging process being dynamic. Thiscould be because the dynamic subnetwork was inferred using a naive Inducedsubgraph approach. Instead, we recently inferred a dynamic aging-specificsubnetwork using a methodologically more advanced notion of network propagation(NP), which improved upon Induced dynamic aging-specific subnetwork in adifferent task, that of unsupervised analyses of the aging process. Here, weevaluate whether our existing NP-based dynamic subnetwork will improve upon thedynamic as well as static subnetwork constructed by the Induced approach in theconsidered task of supervised prediction of aging-related genes. The existingNP-based subnetwork is unweighted, i.e., it gives equal importance to each ofthe aging-specific PPIs. Because accounting for aging-specific edge weightsmight be important, we additionally propose a weighted NP-based dynamicaging-specific subnetwork. We demonstrate that a predictive machine learningmodel trained and tested on the weighted subnetwork yields higher accuracy whenpredicting aging-related genes than predictive models run on the existingunweighted dynamic or static subnetworks, regardless of whether the existingsubnetworks were inferred using NP or the Induced approach.

Improved supervised prediction of aging-related genes via weighted dynamic network analysis